A Hybrid CT-DEWCA-Based Energy-Efficient Routing Protocol for Data and Storage Nodes in Underwater Acoustic Sensor Networks

Underwater Wireless Sensor Networks (UWSNs) face significant communication and performance challenges due to their harsh and dynamic underwater environment. To address these issues, efficient and robust communication models are essential. This research proposes an energy-aware deployment approach and introduces the Controlled Transmission Enhanced by hybrid model scheme with Differential Evolution, Whale Optimization, and Chaotic Adaptation (CT-DEWCA) for relay selection. This model enhances node placement by implementing a "strategic dual transmission" approach, which employs CT for short-distance data transfer and utilizes the DEW-CA hybrid algorithm to select optimal relay nodes for long-distance communication. The strategy optimizes energy allocation between storage and regular nodes. Simulations show that the CT-DEWCA model outperforms conventional Vector-Based Forwarding (VBF), Hop-by-Hop Vector-Based Forwarding (HH-VBF), Depth-Based Routing (DBR), and, with recent work, Energy-Efficient Artificial Fish Swarm-based Clustering Cognitive Intelligence Protocol (EAFSCCIP). The results show an improvement by an average of 5.4% in packet delivery ratio, 20% reduction in cumulative energy consumption, 6% increase in throughput, and 12% lower packet‐loss rate on average compared to the other protocols. This research offers a robust solution for enhancing UWSN performance and longevity by addressing critical energy efficiency issues and reliable data transmission in underwater environments. The proposed approach demonstrates the utility and potential of bio-inspired optimization techniques to tackle the unique challenges of underwater communication networks.